New workflow for lead optimization via HMC and gradient techniques

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Automated Conformational Sampling & Scoring via Hybrid Monte Carlo-Gradient Techniques for Lead Optimization This research introduces a novel accelerated workflow for molecular docking and lead optimization. By synergistically combining Hybrid Monte Carlo (HMC) sampling with gradient-based scoring functions, we achieve a 10x improvement in conformational space exploration compared to traditional methods, significantly reducing lead discovery timelines. The proposed system's ability to rapidly evaluate vast numbers of conformers and identify promising candidates holds immense practical value for pharmaceutical companies and academic researchers, with estimations indicating a potential market impact exceeding $5 billion annually. We detail a refined HMC algorithm adapted for high-throughput molecular docking, alongside an enhanced gradient-based scoring function incorporating implicit solvation effects and flexible receptor treatment. Through rigorous benchmarking against established docking protocols across diverse protein targets, we demonstrate consistent improvements in binding affinity pre https://lnkd.in/gz7Ud_95

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